References
- Shrestha, Ujjwal. “Automatic liver and tumor segmentation from CT scan images using gabor feature and machine learning algorithms,” PhD diss., University of Toledo, 2018.
- J. Yang, M. Fu, and Y. Hu, “Liver vessel segmentation based on inter-scale V-Net,” Math. Biosci. Eng., Vol. 18, no. 4, pp. 4327–40, 2021. DOI: 10.3934/mbe.2021217
- P. Rajamanickam, and S. E. Darmanayagam and Sunil Retmin Raj Cyril Raj. “Automatic segmentation of liver from abdominal computed tomography images using energy feature,” CMC – Comput. Mater. Continua, Vol. 67, no. 1, pp. 709–22, 2021. DOI: 10.32604/cmc.2021.014347
- L. Meng, Q. Zhang, and S. Bu, “Two-stage liver and tumor segmentation algorithm based on convolutional neural network,” Diagnostics, Vol. 11, no. 10, pp. 1806, 2021. DOI: 10.3390/diagnostics11101806
- X. Lu, Q. Xie, Y. Zha, and D. Wang, “Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images,” Sci. Rep., Vol. 8, no. 1, pp. 1–9, 2018. DOI:10.1002/mp.13735.
- X. Guo, L. H. Schwartz, and B. Zhao, “Automatic liver segmentation by integrating fully convolutional networks into active contour models,” Med. Phys., Vol. 46, no. 10, pp. 4455–69, 2019. DOI: 10.1002/mp.13735
- S. K. Siri, S. Pramod Kumar, and M. V. Latte, “Threshold-based new segmentation model to separate the liver from CT scan images,” IETE. J. Res., 1–8, 2020. DOI: 10.1080/03772063.2020.1795938
- S. K. Siri, and M. V. Latte, “A novel approach to extract exact liver image boundary from abdominal CT scan using neutrosophic set and fast marching method,” J. Intell. Syst., Vol. 28, no. 4, pp. 517–32, 2019. DOI: 10.1515/jisys-2017-0144
- M. Liao, Y.-q. Zhao, X.-y. Liu, Y.-z. Zeng, B.-j. Zou, X.-f. Wang, and F. Y. Shih, “Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching,” Comput. Methods Programs Biomed., Vol. 143, pp. 1–12, 2017. DOI: 10.1016/j.cmpb.2017.02.015
- K. Wang, A. Mamidipalli, T. Retson, N. Bahrami, K. Hasenstab, K. Blansit, E. Bass, et al., “Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network,” Radiol. Artif. Intell., Vol. 1, no. 2, pp. 180022, 2019. DOI: 10.1148/ryai.2019180022
- O. I. Alirr, “Deep learning and level set approach for liver and tumor segmentation from CT scans,” J. Appl. Clin. Med. Phys., Vol. 21, no. 10, pp. 200–9, 2020. DOI: 10.1002/acm2.13003
- P. F. Christ, F. Ettlinger, F. Grün, M. Ezzeldin A, J. L. Elshaera, S. Schlecht, F. Ahmaddy, et al., “Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks,” Med. Image. Anal., Vol. v2, pp. 1–20, 2017.
- Y. Yuan, Y. Ren, X. Liu, and J. Wang, “Approach to image segmentation based on interval neutrosophic set,” Infin. Study, 2020. DOI:10.3934/naco.2019028.
- P. Bilic, P. F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, et al., “The liver tumor segmentation benchmark (lits),” ArXiv, Vol. abs/1901.04056, pp. 1–43, 2019. https://arxiv.org/pdf/1901.04056.pdf.
- A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. Ser. B, Vol. 39, no. 1, pp. 1–38, 1977. JSTOR 2984875. MR 0501537. http://www.jstor.org/stable/2984875.
- J. B. MacQueen, “Some Methods for classification and analysis of multivariate observations,” in Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967. 1. University of California Press. pp. 281–97. MR 0214227. Zbl 0214.46201. Retrieved 2009-04-07.
- J. Min, M. Powell, and K. W. Bowyer, “Automated performance Evaluation of range image segmentation algorithms,” IEEE Tran. Syst., man, Cybern.—Part B: Cybern., Vol. 34, no. 1, pp. 263–71, 2004. DOI: 10.1109/TSMCB.2003.811118
- A. A. Taha, and A. Hanbury, “Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool,” BMC Med. Imaging, Vol. 15, no. 1, pp. 1–28, 2015. DOI: 10.1186/s12880-015-0042-7
- T. Heimann, et al., “Comparison and evaluation of methods for liver segmentation from CT datasets,” IEEE Trans. Med. Imaging, Vol. 28, no. 8, 2009. DOI: 10.1109/TMI.2009.2013851